Worked Example: PCA on SNPs data from a vcf file Part 1 - Data Preparation
2022-11-19
Introduction
In this worked example you will replicate a PCA on a published dataset.
The example is split into 2 Parts:
- Part 1: Data Preparation (this file)
- Part 2: Data analysis with PCA
In this Data Preparation phase, you will do the following things:
- Load the SNP genotypes in .vcf format
(
vcfR::read.vcfR()) - Extract the genotypes into an R-compatible format
(
vcfR::extract.gt()) - Rotate the data into the standard R analysis format
(
t()) - Remove individuals (rows) from the data set that have >50% NAs (using a function I wrote)
- Remove SNPs (columns) that are fixed
- Impute remaining NAs (using a
for()loop) - Save the prepared data as a
.csvfile for the next step (write.csv())
Biological background
This worked example is based on a paper in the journal Molecular Ecology from 2017 by Jennifer Walsh titled Subspecies delineation amid phenotypic, geographic and genetic discordance in a songbird.
The study investigated variation between two bird species in the genus Ammodramus: A. nenlsoni and A. caudacutus.
The species A. nenlsoni has been divided into 3 sub-species: A. n. nenlsoni, A.n. alterus, and A n. subvirgatus. The other species, A. caudacutus, has been divided into two subspecies, A.c. caudacutus and A.c. diversus.
The purpose of this study was to investigate to what extent these five subspecies recognized by taxonomists are supported by genetic data. The author’s collected DNA from 75 birds (15 per subspecies) and genotyped 1929 SNPs. They then analyzed the data with Principal Components Analysis (PCA), among other genetic analyzes.
This tutorial will work through all of the steps necessary to re-analyze Walsh et al.s data
Tasks
In the code below all code is provided. Your tasks will be to do 2 things:
- Give a meaningful title to all sections marked “TODO: TITLE”
- Write 1 to 2 sentences describing what is being done and why in all sections marked “TODO: EXPLAIN”
Preliminaries
Load the vcfR and other packages with library().
library(vcfR) ##
## ***** *** vcfR *** *****
## This is vcfR 1.13.0
## browseVignettes('vcfR') # Documentation
## citation('vcfR') # Citation
## ***** ***** ***** *****library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.6-4library(ggplot2)
library(ggpubr)Make sure that your working directory is set to the location of the
file all_loci.vcf.
getwd()## [1] "/Users/vinishasant/Documents/Fall2022/BIOSC1540/CompBioCode"list.files()## [1] "07-mean_imputation.html"
## [2] "07-mean_imputation.Rmd"
## [3] "08-PCA_worked.html"
## [4] "08-PCA_worked.Rmd"
## [5] "09-PCA_worked_example-SNPs-part1.Rmd"
## [6] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [7] "10-PCA_worked_example-SNPs-part2.Rmd"
## [8] "2.136483646-136733646.ALL.chr2_GRCh38.genotypes.20170504.vcf.gz"
## [9] "all_loci.vcf"
## [10] "bird_snps_remove_NAs.html"
## [11] "bird_snps_remove_NAs.Rmd"
## [12] "code_checkpoint_vcfR.html"
## [13] "code_checkpoint_vcfR.Rmd"
## [14] "df.csv"
## [15] "FinalProject"
## [16] "fst_exploration_in_class-STUDENT.html"
## [17] "fst_exploration_in_class-STUDENT.Rmd"
## [18] "fst_exploration_in_class.Rmd"
## [19] "removing_fixed_alleles.html"
## [20] "removing_fixed_alleles.Rmd"
## [21] "rsconnect"
## [22] "test.html"
## [23] "test.Rmd"
## [24] "transpose_VCF_data.html"
## [25] "transpose_VCF_data.Rmd"
## [26] "vcfR_test.vcf"
## [27] "vcfR_test.vcf.gz"
## [28] "vegan_PCA_amino_acids-STUDENT.html"
## [29] "vegan_PCA_amino_acids-STUDENT.Rmd"
## [30] "walsh2017morphology.csv"
## [31] "working_directory_practice.html"
## [32] "working_directory_practice.Rmd"list.files(pattern = "vcf")## [1] "1.159051856-159301856.ALL.chr1_GRCh38.genotypes.20170504.vcf.gz"
## [2] "2.136483646-136733646.ALL.chr2_GRCh38.genotypes.20170504.vcf.gz"
## [3] "all_loci.vcf"
## [4] "code_checkpoint_vcfR.html"
## [5] "code_checkpoint_vcfR.Rmd"
## [6] "vcfR_test.vcf"
## [7] "vcfR_test.vcf.gz"Data preparation
TODO: Loading Data
TODO: Read in information from the vcf file “all_loci.vcf” and store it into a vcfR object called snps.
snps <- vcfR::read.vcfR("all_loci.vcf", convertNA = TRUE)## Scanning file to determine attributes.
## File attributes:
## meta lines: 8
## header_line: 9
## variant count: 1929
## column count: 81
##
Meta line 8 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 1929
## Character matrix gt cols: 81
## skip: 0
## nrows: 1929
## row_num: 0
##
Processed variant 1000
Processed variant: 1929
## All variants processedTODO: Extracting Genotypes
TODO: Use the vcfR extract.gt() function to extract genotype scores from the vcf file and store this information into an object called snps_num
snps_num <- vcfR::extract.gt(snps,
element = "GT",
IDtoRowNames = F,
as.numeric = T,
convertNA = T,
return.alleles = F)TODO: Transposing Data
TODO: Transpose the data using the t() function to switch rows and columns so that the SNPs are found in the columns and samples are in rows.
snps_num_t <- t(snps_num) TODO: Convert the transposed object, which is current a matrix, into a data frame and store it into an object named snps_num_df
snps_num_df <- data.frame(snps_num_t) TODO: Dealing with NAs
TODO: The following is a function that finds all the NAs in the dataframe using the is.na() and which() functions. is.na() will tell you if the value is an NA, and which() can be used to find all the indices where is.na == TRUE. The function returns all the indices where NAs are found.
find_NAs <- function(x){
NAs_TF <- is.na(x)
i_NA <- which(NAs_TF == TRUE)
N_NA <- length(i_NA)
cat("Results:",N_NA, "NAs present\n.")
return(i_NA)
}TODO: This for-loop iterates through every row in the data frame, looking for NAs using the function written above. For each row in the data frame, it finds the location of the NAs, determines how many NAs there were using the length() function, and keeps track of the number of NAs in each row through the object called N_NA.
# N_rows
# number of rows (individuals)
N_rows <- nrow(snps_num_t)
# N_NA
# vector to hold output (number of NAs)
N_NA <- rep(x = 0, times = N_rows)
# N_SNPs
# total number of columns (SNPs)
N_SNPs <- ncol(snps_num_t)
# the for() loop
for(i in 1:N_rows){
# for each row, find the location of
## NAs with snps_num_t()
i_NA <- find_NAs(snps_num_t[i,])
# then determine how many NAs
## with length()
N_NA_i <- length(i_NA)
# then save the output to
## our storage vector
N_NA[i] <- N_NA_i
}## Results: 28 NAs present
## .Results: 20 NAs present
## .Results: 28 NAs present
## .Results: 24 NAs present
## .Results: 23 NAs present
## .Results: 63 NAs present
## .Results: 51 NAs present
## .Results: 38 NAs present
## .Results: 34 NAs present
## .Results: 24 NAs present
## .Results: 48 NAs present
## .Results: 21 NAs present
## .Results: 42 NAs present
## .Results: 78 NAs present
## .Results: 45 NAs present
## .Results: 21 NAs present
## .Results: 42 NAs present
## .Results: 34 NAs present
## .Results: 66 NAs present
## .Results: 54 NAs present
## .Results: 59 NAs present
## .Results: 52 NAs present
## .Results: 47 NAs present
## .Results: 31 NAs present
## .Results: 63 NAs present
## .Results: 40 NAs present
## .Results: 40 NAs present
## .Results: 22 NAs present
## .Results: 60 NAs present
## .Results: 48 NAs present
## .Results: 961 NAs present
## .Results: 478 NAs present
## .Results: 59 NAs present
## .Results: 26 NAs present
## .Results: 285 NAs present
## .Results: 409 NAs present
## .Results: 1140 NAs present
## .Results: 600 NAs present
## .Results: 1905 NAs present
## .Results: 25 NAs present
## .Results: 1247 NAs present
## .Results: 23 NAs present
## .Results: 750 NAs present
## .Results: 179 NAs present
## .Results: 433 NAs present
## .Results: 123 NAs present
## .Results: 65 NAs present
## .Results: 49 NAs present
## .Results: 192 NAs present
## .Results: 433 NAs present
## .Results: 66 NAs present
## .Results: 597 NAs present
## .Results: 1891 NAs present
## .Results: 207 NAs present
## .Results: 41 NAs present
## .Results: 268 NAs present
## .Results: 43 NAs present
## .Results: 110 NAs present
## .Results: 130 NAs present
## .Results: 90 NAs present
## .Results: 271 NAs present
## .Results: 92 NAs present
## .Results: 103 NAs present
## .Results: 175 NAs present
## .Results: 31 NAs present
## .Results: 66 NAs present
## .Results: 64 NAs present
## .Results: 400 NAs present
## .Results: 192 NAs present
## .Results: 251 NAs present
## .Results: 69 NAs present
## .Results: 58 NAs present
## .TODO: This code makes a histogram of the frequency of NAs found in each row. The cutoff50 line, added to the histogram using abline(), represents the point where the number of NAs is equal to 50% of the number of SNPs. Any rows with more NAs than 50% of the number of SNPs will be eliminated from the analysis.
# 50% of N_SNPs
cutoff50 <- N_SNPs*0.5
hist(N_NA)
abline(v = cutoff50,
col = 2,
lwd = 2,
lty = 2)
TODO: This code calculates the percent NAs found in each row and stores it into percent_NA. Using which(), it finds all the rows that contain greater than 50% NAs, and then eliminates them from the dataset through negative indexing.
percent_NA <- N_NA/N_SNPs*100
# Call which() on percent_NA
i_NA_50percent <- which(percent_NA > 50)
snps_num_t02 <- snps_num_t[-i_NA_50percent, ]TODO: Determining Sampling Locations
TODO: The code below goes through a series of steps to remove “unnecessary” information from the row names so that you end with a vector, pop_id, that just contains the population codes. The table() function summarizes the output.
row_names <- row.names(snps_num_t02) # Key
row_names02 <- gsub("sample_","",row_names)
sample_id <- gsub("^([ATCG]*)(_)(.*)",
"\\3",
row_names02)
pop_id <- gsub("[01-9]*",
"",
sample_id)
table(pop_id) ## pop_id
## Alt Cau Div Nel Sub
## 15 12 15 15 11TODO: Function to Remove Invariant Columns
TODO: The following function will go through the data frame and remove invariant columns and return the object with the removed columns, as well as output the number of columns removed.
invar_omit <- function(x){
cat("Dataframe of dim",dim(x), "processed...\n")
sds <- apply(x, 2, sd, na.rm = TRUE)
i_var0 <- which(sds == 0)
cat(length(i_var0),"columns removed\n")
if(length(i_var0) > 0){
x <- x[, -i_var0]
}
## add return() with x in it
return(x)
}
snps_no_invar <- invar_omit(snps_num_t02) ## Dataframe of dim 68 1929 processed...
## 591 columns removedTODO: Mean Imputation
TODO: For each column in the data frame (after invariant columns have been removed), the following code will calculate the mean of the column, find all the NAs, and then replace the NAs with the mean value of the column.
snps_noNAs <- snps_no_invar
N_col <- ncol(snps_no_invar)
for(i in 1:N_col){
# get the current column
column_i <- snps_noNAs[, i]
# get the mean of the current column
mean_i <- mean(column_i, na.rm = TRUE)
# get the NAs in the current column
NAs_i <- which(is.na(column_i))
# record the number of NAs
N_NAs <- length(NAs_i)
# replace the NAs in the current column
column_i[NAs_i] <- mean_i
# replace the original column with the
## updated columns
snps_noNAs[, i] <- column_i
}Save the data
Save the data as a .csv file which can be loaded again later.
write.csv(snps_noNAs, file = "SNPs_cleaned.csv",
row.names = F)Check for the presence of the file with list.files()
list.files(pattern = ".csv")## [1] "df.csv" "SNPs_cleaned.csv"
## [3] "walsh2017morphology.csv"Next steps:
In Part 2, we will re-load the SNPs_cleaned.csv file and
carry an an analysis with PCA.